--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: korca/bae-roberta-base-boolq model-index: - name: korca_bae-roberta-base-boolq-finetuned-lora-tweet_eval_irony results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: irony split: validation args: irony metrics: - type: accuracy value: 0.6712041884816754 name: accuracy --- # korca_bae-roberta-base-boolq-finetuned-lora-tweet_eval_irony This model is a fine-tuned version of [korca/bae-roberta-base-boolq](https://huggingface.co/korca/bae-roberta-base-boolq) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6712 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4942 | None | 0 | | 0.5864 | 0.7118 | 0 | | 0.5885 | 0.6780 | 1 | | 0.6 | 0.6617 | 2 | | 0.6230 | 0.6414 | 3 | | 0.6513 | 0.6204 | 4 | | 0.6335 | 0.6058 | 5 | | 0.6461 | 0.5959 | 6 | | 0.6712 | 0.5967 | 7 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2